def test_basic(self):
n = 20
m = 10
sampler = CRPSampler(0)
sampler.set_dataset(Dataset(n, m))
collection = sampler.sample_collection(5, 2, alpha=0.3, theta=1.0)
print(collection['support'])
print(collection['query'])
def test_basic(self):
folder = '/mnt/local/data/omniglot'
omniglot = OmniglotDataset(folder, 'train')
preprocessor = NormalizationPreprocessor()
for bsize in [1, 2]:
sampler = CRPSampler(0)
sampler2 = SemiSupervisedEpisodeSampler(sampler, 0)
it = SemiSupervisedEpisodeIterator(
omniglot,
sampler2,
batch_size=bsize,
nclasses=10,
nquery=5,
preprocessor=preprocessor,
expand=True,
fix_unknown=True,
label_ratio=0.5,
nd=5,
sd=1,
md=2,
alpha=0.5,
theta=1.0)
for x in range(2):
b = it.next()
print(b)
print('support', tf.reduce_max(b.train_images),
tf.reduce_min(b.train_images), tf.shape(b.train_images))
print('support label', b.train_labels, tf.shape(b.train_labels))
print('support gt', b.train_groundtruth, tf.shape(b.train_groundtruth))
print('query', tf.reduce_max(b.test_images),
tf.reduce_min(b.test_images), tf.shape(b.test_images))
print('query label', b.test_labels, tf.shape(b.test_labels))
def test_markov_hierarchy(self):
k = 10
n = 100
m = 100
subsampler = CRPSampler(0)
blender = MarkovSwitchBlender(np.ones([3]) / 3.0, 0.5, 0)
sampler = HierarchicalEpisodeSampler(subsampler, blender, True, 0)
sampler.set_dataset(Dataset(k, n, m))
collection = sampler.sample_collection(30,
2,
alpha=0.5,
theta=1.0,
nstage=3,
max_num=60,
max_num_per_cls=m)
print(collection['support'])
def test_basic(self):
k = 10
n = 100
m = 100
subsampler = CRPSampler(0)
blender = BlurBlender(window_size=20, stride=5, nrun=10, seed=0)
sampler = HierarchicalEpisodeSampler(subsampler, blender, False, 0)
sampler.set_dataset(Dataset(k, n, m))
for _ in range(10):
collection = sampler.sample_collection(50,
2,
alpha=0.5,
theta=1.0,
nstage=5,
max_num=100,
max_num_per_cls=m)
print(collection['support'])
class SeqCRPSampler(EpisodeSampler):
def __init__(self, seed):
super(SeqCRPSampler, self).__init__(seed)
self._crp_sampler = CRPSampler(seed)
def sample_episode_classes(self,
n,
stages=2,
alpha=0.5,
theta=1.0,
max_num=-1,
max_num_per_cls=20):
"""See EpisodeSampler class for documentation.
Args:
n: Int. Number of classes for each stage.
stages: Int. Number of sequential stages.
alpha: Float. Discount parameter.
theta: Float. Strength parameter.
max_num: Int. Maximum number of images.
max_num_per_class: Int. Maximum number of images per class.
"""
result = []
cur_max = 0
assert n % stages == 0
for i in range(stages):
result_ = self._crp_sampler.sample_episode_classes(
n // stages,
alpha=alpha,
theta=theta,
max_num=max_num,
max_num_per_cls=max_num_per_cls)
result_ = np.array(result_)
# print(result_)
result.extend(list(result_ + cur_max))
cur_max += result_.max() + 1
return result
def test_basic(self):
n = 20
m = 10
sampler = CRPSampler(0)
sampler = SemiSupervisedEpisodeSampler(sampler, 0)
sampler.set_dataset(Dataset(n, m))
for x in range(100):
collection = sampler.sample_collection(
10,
2,
alpha=0.3,
theta=1.0,
max_num_per_cls=m,
max_num=40,
label_ratio=0.5)
s, q = collection['support'], collection['query']
print('Support', s)
print('Query', q)
def test_distractor_basic(self):
n = 20
m = 10
sampler = CRPSampler(0)
sampler = SemiSupervisedEpisodeSampler(sampler, 0)
sampler.set_dataset(Dataset(n, m))
print('start')
print(sampler.cls_dict)
for x in range(100):
print(x)
collection = sampler.sample_collection(
10,
2,
nd=5,
sd=3,
md=2,
alpha=0.3,
theta=1.0,
max_num_per_cls=m,
max_num=40,
label_ratio=0.5)
s, q = collection['support'], collection['query']
def __init__(self, seed): super(SeqCRPSampler, self).__init__(seed) self._crp_sampler = CRPSampler(seed)